Source code for pm4py.algo.discovery.performance_spectrum.variants.log

'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from pm4py.objects.log.util import sorting
from pm4py.objects.log.util import basic_filter
from pm4py.util import points_subset
from pm4py.util import xes_constants as xes
from pm4py.util import exec_utils
from enum import Enum
from pm4py.util import constants
from typing import Optional, Dict, Any, Union, List
from pm4py.objects.log.obj import EventLog




class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
    ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY
    PARAMETER_SAMPLE_SIZE = "sample_size"
    SORT_LOG_REQUIRED = "sort_log_required"





def apply(
    log: EventLog,
    list_activities: List[str],
    sample_size: int,
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[str, Any]:
    """
    Finds the performance spectrum provided a log
    and a list of activities

    Parameters
    -------------
    log
        Log
    list_activities
        List of activities interesting for the performance spectrum (at least two)
    sample_size
        Size of the sample
    parameters
        Parameters of the algorithm,  including:
            - Parameters.ACTIVITY_KEY
            - Parameters.TIMESTAMP_KEY

    Returns
    -------------
    points
        Points of the performance spectrum
    """
    if parameters is None:
        parameters = {}

    activity_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY
    )
    timestamp_key = exec_utils.get_param_value(
        Parameters.TIMESTAMP_KEY, parameters, xes.DEFAULT_TIMESTAMP_KEY
    )
    sort_log_required = exec_utils.get_param_value(
        Parameters.SORT_LOG_REQUIRED, parameters, True
    )

    parameters[Parameters.ATTRIBUTE_KEY] = activity_key
    log = basic_filter.filter_log_events_attr(
        log, list_activities, parameters=parameters
    )
    if sort_log_required:
        log = sorting.sort_timestamp_log(log, timestamp_key=timestamp_key)

    points = []

    for trace in log:
        for i in range(len(trace) - len(list_activities) + 1):
            acti_comb = [
                event[activity_key]
                for event in trace[i: i + len(list_activities)]
            ]

            if acti_comb == list_activities:
                timest_comb = [
                    event[timestamp_key].timestamp()
                    for event in trace[i: i + len(list_activities)]
                ]

                points.append(timest_comb)

    points = sorted(points, key=lambda x: x[0])

    if len(points) > sample_size:
        points = points_subset.pick_chosen_points_list(sample_size, points)

    return points